π Methodology & Data Sources
This comprehensive resilience dashboard integrates multiple data sources, advanced statistical models, and forecasting techniques to provide a holistic view of global resilience from 2019 to 2030.
1. Data Sources
Our analysis combines data from the following authoritative sources:
| Source |
Indicators |
Coverage |
Update Frequency |
| World Bank API |
24 indicators across 4 pillars |
253 economies |
Annual |
| INFORM Risk Index |
Hazard, vulnerability, coping capacity |
71 countries |
Bi-annual |
| GeoJSON |
Country boundaries |
258 features |
Static |
2. Four Pillars of Resilience
Resilience is assessed across four interconnected pillars, each comprising 6 key indicators:
π° Financial Pillar
- GDP Growth: Annual percentage growth rate of GDP at market prices
- Debt-to-GDP Ratio: Central government debt as % of GDP
- Forex Reserves: Total reserves in months of imports
- Trade Balance: Exports minus imports as % of GDP
- FDI Inflows: Foreign direct investment, net inflows (% of GDP)
- Inflation Rate: Consumer price index (annual %)
π₯ Social Pillar
- Gini Index: Income inequality measure (0 = perfect equality, 100 = perfect inequality)
- Life Expectancy: Life expectancy at birth (years)
- Education Index: Mean years of schooling
- Unemployment Rate: % of total labor force
- Poverty Headcount: % living below $2.15/day
- Health Expenditure: Current health expenditure (% of GDP)
ποΈ Institutional Pillar
- Government Effectiveness: World Governance Indicators
- Rule of Law: Perception of law enforcement quality
- Control of Corruption: Public power exercise for private gain
- Regulatory Quality: Ability to formulate sound policies
- Political Stability: Likelihood of political instability
- Voice & Accountability: Democratic participation
ποΈ Infrastructure Pillar
- Electric Power Consumption: kWh per capita
- Internet Access: % of population with access
- Mobile Subscriptions: Per 100 people
- Road Quality: Quality of road infrastructure index
- Water Access: % with access to safely managed drinking water
- Sanitation: % with access to safely managed sanitation
3. Score Calculation Methodology
Each pillar score is calculated using min-max normalization with directional adjustments:
Score = Ξ£ (wi Γ normalized_indicatori) / Ξ£ wi
Where:
β’ wi = weight for indicator i (equal weighting: 1/6)
β’ normalized_indicatori = (value - min) / (max - min)
β’ For negative indicators (e.g., debt, inflation): 1 - normalized value
The Overall Resilience Score is the arithmetic mean of the four pillar scores:
Overall Score = (Financial + Social + Institutional + Infrastructure) / 4
4. Forecasting Models
We employ two complementary time-series models for 2026-2030 forecasts:
4.1 BSTS (Bayesian Structural Time Series)
BSTS decomposes time series into interpretable components using Bayesian inference:
yt = ΞΌt + Ξ²'xt + Ξ΅t
Where:
β’ yt = observed value at time t
β’ ΞΌt = local level (trend component)
β’ Ξ²'xt = regression component
β’ Ξ΅t ~ N(0, ΟΒ²) = observation noise
State evolution:
β’ ΞΌt = ΞΌt-1 + Ξ΄t-1 + Ξ·t
β’ Ξ΄t = Ξ΄t-1 + ΞΆt
β’ Ξ·t ~ N(0, ΟΞ·Β²), ΞΆt ~ N(0, ΟΞΆΒ²)
4.2 DFM (Dynamic Factor Model)
DFM extracts latent factors capturing common dynamics across pillars:
Xt = Ξft + et
Where:
β’ Xt = observed data matrix at time t
β’ Ξ = factor loading matrix
β’ ft = k latent factors (k = 2)
β’ et = idiosyncratic errors
Factor evolution:
β’ ft = Ξ¦ft-1 + ut
β’ ut ~ N(0, Q)
4.3 Zero Percentile Weighting
To emphasize extreme performers, we apply Zero Percentile Weighting:
wi = 1 - |percentilei - 0.5| Γ 2
Where:
β’ percentilei = country i's rank position (0-1)
β’ Countries at 0th or 100th percentile: w = 1 (full weight)
β’ Countries at 50th percentile: w = 0 (no weight)
β’ Non-linear emphasis on tails of distribution
Final forecast combines both models:
Forecastt = Ξ± Γ BSTSt + (1-Ξ±) Γ DFMt
Where Ξ± = 0.6 (60% BSTS, 40% DFM)
5. Historical Data Generation
For 2019-2024, we generate plausible historical trajectories using controlled random walks:
valuet = current_value Γ (1 + trend Γ years_back + volatility Γ Ξ΅)
Where:
β’ current_value = 2025 baseline score
β’ trend ~ U(-0.01, 0.015) = annual drift rate
β’ volatility ~ U(0.02, 0.08) = year-to-year noise
β’ years_back = distance from 2025
β’ Ξ΅ ~ N(0, 1) = standard normal noise
6. Color Coding System
Countries are visualized using a 5-color percentile-based gradient:
| Color |
Threshold |
Interpretation |
Typical Count |
| β Dark Green |
> 0.66 |
Excellent resilience |
~65 countries |
| β Light Green |
0.60 - 0.66 |
Good resilience |
~50 countries |
| β Yellow |
0.53 - 0.60 |
Moderate resilience |
~55 countries |
| β Orange |
0.45 - 0.53 |
Low resilience |
~50 countries |
| β Red |
< 0.45 |
Critical vulnerability |
~50 countries |
7. Data Quality & Limitations
- Missing Data: Some indicators unavailable for all countries; mean imputation used when appropriate
- Data Latency: Most recent World Bank data is 2022-2023; 2025 values extrapolated
- Forecast Uncertainty: 95% confidence intervals provided; actual outcomes may vary significantly
- Historical Simulation: 2019-2024 values are generated trajectories, not actual historical data
- Model Assumptions: Assumes continuation of current trends; does not account for shocks (wars, pandemics, etc.)
8. Technical Implementation
The dashboard is built using modern web technologies:
- Frontend: Pure HTML5/CSS3/JavaScript (no frameworks)
- Mapping: Leaflet.js 1.9.4 for interactive choropleth maps
- Charts: Chart.js 4.4.0 for responsive graphs
- Backend: Python 3.9 with pandas, numpy, statsmodels, scikit-learn
- Data Format: All data embedded as JSON (no external API calls)
- File Size: ~2-3 MB total (can be shared as single HTML file)
9. Citation & Attribution
Data Sources:
- World Bank Open Data API (https://data.worldbank.org/)
- INFORM Risk Index 2025 (https://drmkc.jrc.ec.europa.eu/inform-index)
- Natural Earth GeoJSON (https://www.naturalearthdata.com/)
Suggested Citation:
Global Resilience Dashboard (2026). Integrated analysis of 253 World Bank recognized economies using World Bank indicators, INFORM Risk data, BSTS forecasting, and Dynamic Factor Models. Historical data (2019-2025) and forecasts (2026-2030).
10. Version & Updates
Version: 2.0 (January 2026)
Last Updated: 16 January 2026
Next Scheduled Update: July 2026 (with new World Bank data release)